Learning device, learning method, and program

ABSTRACT

Disclosed is a learning device. A feature-quantity calculation unit extracts a feature quantity from each feature point of a learning image. An acquisition unit acquires a classifier already obtained by learning as a transfer classifier. A classifier generation unit substitutes feature quantities into weak classifiers constituting the transfer classifier, calculates error rates of the weak classifiers on the basis of classification results of the weak classifiers and a weight of the learning image, and iterates a process of selecting a weak classifier of which the error rate is minimized a plurality of times. In addition, the classifier generation unit generates a classifier for detecting a detection target by linearly coupling a plurality of selected weak classifiers.

BACKGROUND

The present technology relates to a learning device, a learning method,and a program, and more particularly, to a learning device, a learningmethod, and a program capable of obtaining a highly accurate classifierat a higher speed.

Although a large number of learning images for hand shapes arenecessary, for example, in learning of a multi-class object recognizersuch as a hand-shape detector, the learning is time-consuming if a largenumber of learning images are used. Transfer learning capable ofreducing a learning time using previously obtained knowledge has beenproposed (for example, see L. Torrey and J. Shavlik, “TransferLearning,” In E. Soria, J. Martin, R. Magdalena, M. Martinez and A.Serrano, editors, Handbook of Research on Machine Learning Applications,IGI Global 2009; and Sinno Jialin Pan and Qiang Yang, “A Survey onTransfer Learning,” IEEE Transactions on Knowledge and Data Engineering,Vol. 22, No. 10, pp 1345 to 1359, October 2010).

In addition, recently, object recognition systems using transferlearning have been proposed (for example, see L. Fei-Fei, R. Fergus andP. Perona, “One-Shot learning of object categories,” IEEE Trans. PatternAnalysis and Machine Intelligence, Vol. 28, No. 4, pp 594 to 611, 2006;E. Bart, S. Ullman, “Cross-generalization: learning novel classes from asingle example by feature replacement,” in Proc. CVPR, 2005; and M.Stark, M. Goesele and B. Schiele, “A Shape-Based Object Class Model forKnowledge Transfer,” Twelfth IEEE International Conference on ComputerVision (ICCV), 2009, Kyoto, Japan (2009)).

In these object recognition systems, objects are expressed by smallparts and appearance and location distributions of the parts are learnedand unknown classes are learned by transferring distributions of knownclasses. In addition, the object recognition systems use a framework ofBayesian estimation and focus on learning of one or more samples or asmall number of samples.

SUMMARY

However, it may be impossible to obtain sufficient performance if thereare not many learning samples for robust object detection in the realworld.

It is desirable to obtain a highly accurate classifier at a higherspeed.

According to the present embodiment, there is provided a learning deviceincluding a feature-quantity extraction unit for extracting a featurequantity from a feature point of a learning image with respect to eachof a plurality of learning images including a learning image including adetection target and a learning image not including the detectiontarget, a weak-classification calculation unit for calculating aclassification result of the detection target according to a weakclassifier for every learning image by substituting the feature quantitycorresponding to the weak classifier into the weak classifier withrespect to each of a plurality of weak classifiers constituting atransfer classifier, which is a classifier for detecting the detectiontarget obtained by statistical learning, and a classifier generationunit for generating the classifier for detecting the detection targetusing the weak classifier selected from the plurality of weakclassifiers on the basis of the classification result.

The learning device may further include a weight setting unit forsetting a weight of the learning image based on the classificationresult, and an error-rate calculation unit for calculating an error rateof the weak classifier based on the classification result of eachlearning image according to the weak classifier and the weight, whereinthe classifier generation unit selects the weak classifier based on theerror rate.

The classifier generated by the classifier generation unit may be usedfor multi-class object recognition.

The classifier generated by the classifier generation unit is aclassifier constituting a classifier of a tree structure, and thetransfer classifier is a classifier constituting a leaf of theclassifier of the tree structure.

A learning method or a program according to the first aspect of thepresent technology extracts a feature quantity from a feature point of alearning image with respect to each of a plurality of learning imagesincluding a learning image including a detection target and a learningimage not including the detection target, calculates a classificationresult of the detection target according to a weak classifier for everylearning image by substituting the feature quantity corresponding to theweak classifier into the weak classifier with respect to each of aplurality of weak classifiers constituting a transfer classifier, whichis a classifier for detecting the detection target obtained bystatistical learning, and detects the detection target using the weakclassifier selected from the plurality of weak classifiers on the basisof the classification result, the learning method including extracting,by the feature-quantity extraction unit, the feature quantity from thelearning image; and calculates, by the weak-classification calculationunit, the classification result, and generates, by the classifiergeneration unit, the classifier.

According to the first aspect of the present technology, there isprovided a learning device including a feature-quantity extraction unitfor extracting a feature quantity from a feature point of a learningimage with respect to each of a plurality of learning images including alearning image including a detection target and a learning image notincluding the detection target, a weak-classification calculation unitfor calculating a classification result of the detection targetaccording to a weak classifier for every learning image by substitutingthe feature quantity corresponding to the weak classifier into the weakclassifier with respect to each of a plurality of weak classifiersconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak classifiers on the basis of the classification result, thelearning method including extracting, by the feature-quantity extractionunit, the feature quantity from the learning image; calculating, by theweak-classification calculation unit, the classification result; andgenerating, by the classifier generation unit, the classifier.

According to the second aspect of the present technology, there isprovided a learning device including a feature-quantity extraction unitfor extracting a feature quantity from a feature point of a learningimage with respect to each of a plurality of learning images including alearning image including a detection target and a learning image notincluding the detection target, a weak-classification calculation unitfor calculating a classification result of the detection targetaccording to a weak classifier for every learning image by substitutingthe feature quantity corresponding to the weak classifier into the weakclassifier with respect to each of a plurality of weak classifiersconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak classifiers on the basis of the classification result, thelearning method including extracting, by the feature-quantity extractionunit, the feature quantity from the learning image; calculating, by theweak-classification calculation unit, the classification result; andgenerating, by the classifier generation unit, the classifier.

The learning device may further include a weight setting unit forsetting a weight of the learning image based on the classificationresult; and an error-rate calculation unit for calculating an error rateof the weak classifier based on the classification result of eachlearning image according to the weak classifier and the weight. Theclassifier generation unit selects the weak classifier based on theerror rate.

The classifier generated by the classifier generation unit is used formulti-class object recognition.

The classifier generated by the classifier generation unit is aclassifier constituting a classifier of a tree structure, and thetransfer classifier is a classifier constituting a leaf of theclassifier of the tree structure.

A learning method for use in a learning device including afeature-quantity extraction unit for extracting a feature quantity froma feature point of a learning image with respect to each of a pluralityof learning images including a learning image including a detectiontarget and a learning image not including the detection target, aweak-classifier setting unit for generating a weak classifier based onthe feature quantity corresponding to a transfer weak classifierconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, among featurequantities extracted from the learning image and the learning image, aweak-classification calculation unit for calculating a classificationresult of the detection target according to the weak classifier forevery learning image by substituting the feature quantity correspondingto the weak classifier into the weak classifier, and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak

According to the second aspect of the present technology, there isprovided a program for causing a computer to execute extracting afeature quantity from a feature point of a learning image with respectto each of a plurality of learning images including a learning imageincluding a detection target and a learning image not including thedetection target, generating a weak classifier based on the featurequantity corresponding to a transfer weak classifier constituting atransfer classifier, which is a classifier for detecting the detectiontarget obtained by statistical learning, among feature quantitiesextracted from the learning image and the learning image. calculating aclassification result of the detection target according to the weakclassifier for every learning image by substituting the feature quantitycorresponding to the weak classifier into the weak classifier, andgenerating the classifier for detecting the detection target using theweak classifier selected from the plurality of weak classifiers on thebasis of the classification result.

According to the first and second embodiments of the present technologyas described above, a highly accurate classifier can be obtained at ahigher speed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an overview of the present technology;

FIG. 2 is a diagram illustrating learning by the transfer of parameters;

FIG. 3 is a diagram illustrating learning by the transfer of featurequantities;

FIG. 4 is a diagram illustrating a configuration example of anembodiment of a hand-shape classification system;

FIG. 5 is a diagram illustrating a configuration example of a classifiergeneration unit;

FIG. 6 is a flowchart illustrating a transfer learning process;

FIG. 7 is a diagram illustrating the effect of learning by the transferof parameters;

FIG. 8 is a flowchart illustrating a classification process;

FIG. 9 is a diagram illustrating another configuration example of alearning device;

FIG. 10 is a diagram illustrating a configuration example of aclassifier generation unit;

FIG. 11 is a flowchart illustrating the transfer learning process;

FIG. 12 is a diagram illustrating settings of classifiers;

FIG. 13 is a diagram illustrating settings of classifiers;

FIG. 14 is a diagram illustrating the effect of learning by the transferof feature quantities;

FIG. 15 is a diagram illustrating a classifier of a tree structure;

FIG. 16 is a diagram illustrating a configuration example of arecognition device;

FIG. 17 is a flowchart illustrating a classification process; and

FIG. 18 is a diagram illustrating a configuration example of a computer.

DETAILED DESCRIPTION OF THE EMBODIMENT(S)

Hereinafter, preferred embodiments of the present technology will bedescribed in detail with reference to the appended drawings. Note that,in this specification and the appended drawings, structural elementsthat have substantially the same function and structure are denoted withthe same reference numerals, and repeated explanation of thesestructural elements is omitted.

Hereinafter, embodiments to which the present technology is applied willbe described with reference to the drawings.

(Overview of Present Technology)

The present technology aims at generating a classifier to be used torecognize an object of a detection target according to boosting-basedtransfer learning. Although the object of the detection target may beany object such as a human or face, an example in which the detectiontarget is a human hand, particularly, a hand with a predetermined shape,will be described hereinafter.

For example, multi-shape hand detection is a difficult problem in animage recognition task. Because a pattern (hand appearance) is largelyvaried on an image according to hand-shape variation, it is difficult tomodel each hand-shape class. In addition, it is difficult to performrobust recognition in the real world according to illumination variationor partial concealment, perspective variation, background complexity,and the like as general problems in image recognition. In addition, manylearning samples should be provided to handle many shapes.

For example, as illustrated in FIG. 1, transfer learning of hand-shapedetection according to the present technology aims at new learning ofother hand shapes using knowledge already obtained by learning, and isavailable for multi-shape hand detection and the like.

For example, it is assumed that classifiers of hand shapes of a rock,scissors, and paper for use in a rock-paper-scissors game have alreadyused and learned a large number of learning samples as indicated by anarrow A11. Here, the rock is a state of the hand in which all fingersare retracted. The scissors are a state of the hand in which the indexand middle fingers are extended and the remaining fingers are retracted.The paper is a state of the hand in which all the fingers are extended.

If there are classifiers for detecting some hand shapes as describedabove, classifiers of already obtained hand shapes are used for learningwhen a classifier for detecting a hand shape, for example, a shape of apointing hand in which only the index finger is extended, is obtained bylearning.

That is, boosting-based transfer learning is performed using classifiersof hand shapes of the rock, the scissors, and the paper and an image forlearning a pointing hand shape indicated by an arrow A12. A classifierfor detecting a pointing hand shape indicated by an arrow A13 isgenerated.

Here, both a detection target of an already obtained classifier and adetection target of a newly generated classifier are the same object(hand), and only a state of the object such as a shape or direction ofthe hand detected by these classifiers is different. For example, inrock and pointing hand shapes, only an extended index-finger part isdifferent and many other parts such as parts of retracted fingers havesimilar shapes. If knowledge obtained by learning of the classifier ofthe hand shape of the rock is used for learning of a classifier of thepointing hand shape, it is possible to obtain a highly accurateclassifier at a higher speed.

The classifier obtained as described above includes a plurality of weakclassifiers. For example, the weak classifier is a function ofoutputting a determination result of whether or not a predeterminedregion is likely to be a detection target if a feature quantityextracted from the predetermined region of an image is substituted asshown in the following Expression (1).

f _(n)(x)=a _(n) ×g(x>thw _(n))+b _(n)  (1)

In Expression (1), a_(n) and b_(n) are constants. In addition,g(x>thw_(n)) is a function of outputting a numeric value “1” indicatingthat the predetermined region is likely to be the detection target if afeature quantity x is greater than a threshold thw_(n), and outputting anumeric value “0” indicating that the predetermined region is not likelyto be the detection target if the feature quantity x is less than orequal to the threshold thw_(n). Therefore, the predetermined region isdetermined to be likely to be the detection target iff_(n)(x)=a_(n)+b_(n), and the predetermined region is determined not tobe likely to be the detection target if f_(n)(x)=b_(n).

A classifier F(x) including the weak classifier f_(n)(x) as describedabove is expressed, for example, by the following Expression (2).

F(x)=Σf _(n)(x)  (2)

That is, the classifier F(x) is a sum of N weak classifiers f₁(x) tof_(n)(x). In addition, the detection target is determined to be presentin the image if an output value of the classifier F(x) is greater than apredetermined threshold th, and the detection target is determined to beabsent from the image if the output value of the classifier F(x) is lessthan or equal to the predetermined threshold th.

When the new classifier F(x) is generated by boosting-based transferlearning using other classifiers already obtained, the following twotechniques are considered.

(Technique 1) Technique of learning by transferring parameters fromother classifiers

(Technique 2) Technique of learning by transferring feature quantitiesfrom other classifiers

For example, in learning by transferring parameters in Technique 1, apointing-hand detecting classifier F13 is generated from a weakclassifier constituting a rock detecting classifier F11 and a weakclassifier constituting a scissors detecting classifier F12 alreadyobtained by learning as illustrated in FIG. 2.

In the example of FIG. 2, the classifier F11 includes four weakclassifiers f11 to f14, and the classifier F12 includes four weakclassifiers f15 to f18. In transfer learning, some weak classifierssuitable for detecting a pointing hand shape are selected from amongthese eight weak classifiers f11 to f18, and the selected weakclassifiers are linearly coupled to be the classifier F13. Here, theclassifier F13 includes four weak classifiers f11, f13, f17, and f18.

For example, the weak classifier f11 constituting the classifier F13 isa weak classifier that determines rock likelihood using a featurequantity extracted from a part of a retracted little finger of the rockamong the weak classifiers constituting the rock detecting classifierF11. In addition, for example, the weak classifier f17 constituting theclassifier F13 becomes a weak classifier that determines scissorslikelihood using a feature quantity extracted from a part of an extendedindex finger of the scissors among the weak classifiers constituting thescissors detecting classifier F12.

As described above, if the weak classifiers, which determine detectiontarget likelihoods for portions common (similar) to detection targets ofweak classifiers to be newly generated such as the part of the retractedlittle finger and the part of the extended index finger, areappropriately combined, it is possible to more easily obtain a highlyaccurate classifier.

The boosting-based learning is a learning method of configuring onestrong classifier by collecting a plurality of classifiers, which areweak alone, as weak classifiers, and a classifier obtained by learningmay be preferably used for a fast object detection system. The boostingdescribed above is known as adaptive boosting (AdaBoost). In addition,the boosting-based learning is described in detail, for example, in PaulViola & Michael Jones', “Robust real-time Object Detection,”International Journal of Computer Vision 2001.

On the other hand, in learning that transfers feature quantities in theabove-described Technique 2, the learning in which feature quantities ofweak classifiers constituting a rock detecting classifier F21 andfeature quantities of weak classifiers constituting a scissors detectingclassifier F22 already obtained by learning are used as illustrated inFIG. 3 is performed.

That is, the classifier F21 includes four weak classifiers. Theclassifier F21 extracts feature quantities t11 to t14 from an image andthese feature quantities t11 to t14 are substituted into the weakclassifiers. In addition, the classifier F22 includes four weakclassifiers. The classifier F22 extracts feature quantities t15 to t18from an image and these feature quantities t15 to t18 are substitutedinto the weak classifiers.

In the transfer learning of a pointing hand shape classifier F23, somefeature quantities suitable for detecting a pointing hand shape areselected from among the eight feature quantities t11 to t18, and a weakclassifier is generated from the selected feature quantities. That is,the feature quantities suitable for detecting the pointing hand shapeare used, so that parameters such as the constants a_(n) and b_(n) andthe threshold thw_(n) of Expression (1) constituting the weak classifierare re-learned.

In the example of FIG. 3, the feature quantities t11, t16, t13, and t18are selected and weak classifiers f21 to f24 are generated from thesefeature quantities. Specifically, for example, in a plurality oflearning images as samples for use in learning, the feature quantity isextracted in the same method as that of extraction of the featurequantity t11 from the same position as an extraction position of thefeature quantity t11 and the weak classifier f21 is generated using theextracted feature quantity. That is, in further detail, the featurequantity t11 indicates an extraction position and an extraction methodof the feature quantity suitable for detecting the pointing hand shape.If the weak classifiers f21 to f24 are obtained on the basis of selectedfeature quantities, these weak classifiers are linearly coupled to bethe classifier F23.

In the transfer learning illustrated in FIG. 3, a new classifier isgenerated using a feature quantity of an extraction position and anextraction method suitable for detecting a pointing hand shape amongfeature quantities that are substituted into weak classifiers of otherclassifiers already obtained.

Next, specific embodiments will be described in the order of parametertransfer learning of Technique 1 and feature-quantity transfer learningof Technique 2.

First Embodiment

[Configuration Example of Hand-Shape Classification System]

FIG. 4 is a diagram illustrating a configuration example of anembodiment of the hand-shape classification system when the parametertransfer learning of Technique 1 is performed.

The hand-shape classification system includes a learning device 11, aclassifier recording unit 12, and a recognition device 13, and detects ahand of a specific shape as a detection target (target object) from aninput image.

The learning device 11 is used when a process of classifying thepresence/absence of a detection target on the image in the recognitiondevice 13 on the basis of an input learning image is performed,generates a classification feature quantity and a classifier, and causesthe classifier recording unit 12 to record the generated featurequantity and classifier. The recognition device 13 classifies whether ornot there is the detection target in the input image using theclassification feature quantity and the classifier recorded on theclassifier recording unit 12, and outputs its classification result.

The learning device 11 includes a feature-point extraction unit 21, afeature-quantity calculation unit 22, an acquisition unit 23, and aclassifier generation unit 24.

The feature-point extraction unit 21 extracts a feature point to be usedwhen a classifier is generated from an input learning image, andprovides the feature-quantity calculation unit 22 with the extractedfeature point and the learning image. The feature-quantity calculationunit 22 calculates a feature quantity of each feature point on the basisof the learning image from the feature-point extraction unit 21, andprovides the classifier generation unit 24 with the calculated featurequantity and the learning image.

The acquisition unit 23 acquires some classifiers (hereinafter referredto as transfer classifiers), which are related to an object serving asthe same detection target as that of a classifier to be currentlygenerated but related to different states of the object serving as thedetection target, from an external device or the like, and provides theacquired classifiers to the classifier generation unit 24.

The classifier generation unit 24 performs, for example, boosting-basedtransfer learning to generate a classifier that classifies a detectiontarget, on the basis of the learning image and the feature quantityprovided from the feature-quantity calculation unit 22 and the transferclassifier provided from the acquisition unit 23. In addition, bydesignating a feature quantity of a feature point to be used when thedetection target is classified using the generated classifier as aclassification feature quantity, the classifier generation unit 24provides the classifier recording unit 12 with the classifier and theclassification feature quantity and causes the classifier recording unit12 to record the classifier and the classification feature quantity.

In addition, the recognition device 13 includes a feature-pointextraction unit 31, a feature-quantity calculation unit 32, aclassification calculation unit 33, and a classification-result outputunit 34. Because the feature-point extraction unit 31 and thefeature-quantity calculation unit 32 of the recognition device 13perform the same processes as the feature-point extraction unit 21 andthe feature-quantity calculation unit 22 of the learning device 11,description thereof is omitted.

The classification calculation unit 33 reads the classification featurequantity and the classifier recorded on the classifier recording unit12. In addition, the classification calculation unit 33 carries out acalculation by substituting a feature quantity corresponding to theclassification feature quantity among feature quantities from thefeature-quantity calculation unit 32 into the read classifier, andprovides its calculation result to the classification-result output unit34. The classification-result output unit 34 outputs a classificationresult of whether or not the detection target has been detected from theinput image on the basis of the calculation result from theclassification calculation unit 33.

[Configuration Example of Classifier Generation Unit]

In addition, the classifier generation unit 24 of FIG. 4 is configuredas illustrated in FIG. 5 in further detail.

The classifier generation unit 24 includes a weak-classificationcalculation unit 61, an error-rate calculation unit 62, a classifierupdate unit 63, and a weight setting unit 64.

With respect to each weak classifier constituting the transferclassifier from the acquisition unit 23, the weak-classificationcalculation unit 61 substitutes the feature quantity extracted from thefeature point of the learning image provided from the feature-quantitycalculation unit 22 into the weak classifier, and determines whether ornot the detection target has been recognized by the weak classifier.

The error-rate calculation unit 62 calculates an error rate ofrecognition of the detection target by each weak classifier on the basisof the calculation result by each weak classifier provided from the weakclassification calculation unit 61, a label added to the learning imagefrom the feature-quantity calculation unit 22, and a weight for everylearning image from the weight setting unit 64. It is assumed that thelabel indicating whether or not the detection target is included in thelearning image is added to the learning image. For example, the label isadded to the learning image as “1” if the detection target is includedin the learning image, and the label is added to the learning image as“−1” if no detection target is included in the learning image.

The classifier update unit 63 selects some weak classifiers having alowest error rate calculated by the error-rate calculation unit 62 amongweak classifiers constituting transfer classifiers, generates aclassifier including the selected weak classifiers, and provides thegenerated classifier and the classification feature quantity to theclassifier recording unit 12. In addition, the classifier update unit 63calculates a degree of reliability based on the error rate of theselected weak classifier, and provides the calculated reliability degreeto the weight setting unit 64. The weight setting unit 64 updates aweight for every learning image on the basis of the reliability degreefrom the classifier update unit 63, and provides the updated weight tothe error-rate calculation unit 62.

[Description of Transfer Learning Process]

Next, the transfer learning process by the learning device 11 will bedescribed with reference to the flowchart of FIG. 6. The transferlearning process is started if a learning image including a detectiontarget and a learning image not including the detection target areprovided to the learning device 11 and a classifier generationinstruction is generated. That is, a plurality of learning images towhich the label “1” is added and a plurality of learning images to whichthe label “−1” is added are provided to the learning device 11.

In step S11, the acquisition unit 23 acquires a plurality of transferclassifiers and provides the acquired transfer classifiers to theweak-classification calculation unit 61 of the classifier generationunit 24. For example, if a classifier for recognizing a hand of apointing shape is generated thereafter, a rock classifier or a scissorsclassifier generated by statistical learning such as AdaBoost isacquired as a transfer classifier.

In step S12, the feature-point extraction unit 21 extracts somepositions (pixels) as feature points on the learning image for everylearning image provided, and provides the feature-quantity calculationunit 22 with the extracted feature points and the learning image.

In step S13, the feature-quantity calculation unit 22 calculates afeature quantity based on the learning image and the feature pointprovided from the feature-point extraction unit 21.

For example, the feature-quantity calculation unit 22 performs afiltering operation using a filter such as a rectangle filter withrespect to a feature point on the learning image, and designates itsfiltering result as a feature quantity at the feature point. The featurequantity calculation unit 22 calculates a feature quantity of eachfeature point on the learning image for every learning image, providesthe feature quantity of each feature point to the weak-classificationcalculation unit 61, and provides the learning image to the error-ratecalculation unit 62.

In step S14, the weight setting unit 64 initializes a weight for everylearning image. For example, if M learning images P₁ to P_(M) areprovided, a weight W_(m) (where 1≦m≦M) of each learning image becomes1/M. In addition, the classifier update unit 63 initializes a retainedclassifier F(x) to 0.

In step S15, the weak-classification calculation unit 61 substitutes afeature quantity of a feature point provided from the feature-quantitycalculation unit 22 into a weak classifier constituting a transferclassifier provided from the acquisition unit 23, and recognizes(classifies) a detection target.

For example, k transfer classifiers are provided from the acquisitionunit 23 to the weak-classification calculation unit 61, and a sum ofweak classifiers constituting the transfer classifiers is assumed to beN. That is, the N weak classifiers f₁(x) to f_(N)(x) are assumed to beprovided to the weak-classification calculation unit 61. In addition,feature quantities F_(n) (where 1≦n≦N) of N feature points FP_(n)corresponding to the weak classifiers f₁(x) to f_(n)(x) are extractedfrom each learning image P_(m) (where 1≦m≦M) are assumed to beextracted. That is, a feature point FP_(n) corresponding to the weakclassifier f_(n)(x) is a feature point from which a feature quantity tobe substituted into the weak classifier f_(n)(x) is extracted.

In this case, with respect to each weak classifier f_(n)(x) (where1≦n≦N), the weak-classification calculation unit 61 substitutes thefeature quantity F_(n) of the feature point FP_(n) for every learningimage P_(m) into the function g(x>thw_(n)) constituting the weakclassifier f_(n)(x) as a variable x in Expression (1). Its calculationresult becomes a classification result at the feature point FP_(n) inthe learning image P_(m) of the weak classifier f_(n)(x).

That is, if the feature quantity F_(n) is greater than the thresholdthw_(n) of the function g(x>thw_(n)), the detection target is includedat the feature point FP_(n). That is, the detection target is recognizedand the numeric value “1” indicating that the detection target isrecognized becomes the classification result. On the other hand, if thefeature quantity F_(n) is less than or equal to the threshold thw_(n),no detection target is included at the feature point FP_(n). That is, nodetection target is recognized and the numeric value “0” indicating thatno detection target is recognized becomes the classification result. Theweak-classification calculation unit 61 provides the error-ratecalculation unit 62 with the classification result obtained as describedabove.

In step S16, the error-rate calculation unit 62 calculates an error rateof recognition of a detection target by each weak classifier on thebasis of the classification result from the weak-classificationcalculation unit 61, the label added to the learning image from thefeature-quantity calculation unit 22, and the weight for every learningimage from the weight setting unit 64. That is, the error rate E_(n)(where 1≦n≦N) is calculated with respect to each weak classifierf_(n)(x).

Specifically, the error-rate calculation unit 62 compares theclassification result at the feature point FP_(n) for every learningimage P_(m) to the label added to the learning image P_(m) with respectto the weak classifier f_(n)(x), and designates the sum of weights W_(m)of learning images P_(m) of which classification results are falserecognition as the error rate E_(n).

For example, if the classification result at the feature point FP_(n) ofthe learning image P_(m) is “1” but the label of the learning imageP_(m) is “−1,” that is, if no detection target is actually included inthe learning image P_(m) but the detection target is recognized in thelearning image P_(m) by the weak classifier f_(n)(x), recognitionbecomes false. In addition, for example, if the classification result atthe feature point FP_(n) of the learning image P_(m) is “0” but thelabel of the learning image P_(m) is “1,” that is, if the detectiontarget is included in the learning image P_(m) but no detection targetis recognized in the learning image P_(m) by the weak classifierf_(n)(x), recognition becomes false.

The error rate E_(n) of the weak classifier f_(n)(x) obtained asdescribed above indicates the recognition accuracy of the detectiontarget according to the weak classifier f_(n)(x). The less the errorrate E_(n) of the weak classifier f_(n)(x) is, the more the weakclassifier can be suitable to detect the detection target.

When calculating the error rates E_(n) of the weak classifiers f_(n)(x),the error-rate calculation unit 62 provides the calculated error ratesto the classifier update unit 63.

In step S17, the classifier update unit 63 selects the weak classifierf_(n)(x) of which the error rate E_(n) is minimized among the N weakclassifiers f₁(x) to f_(n)(x) on the basis of the error rates E_(n) ofthe weak classifiers f_(n)(x) provided from the error-rate calculationunit 62. That is, the best weak classifier for detecting a hand of apointing shape serving as the detection target is selected. Theclassifier update unit 63 acquires the selected weak classifier f_(n)(x)from the weak-classification calculation unit 61 via the error-ratecalculation unit 62.

In step S18, the classifier update unit 63 updates the classifier byadding the weak classifier f_(n)(x) selected in step S17 to the retainedclassifier F(x).

That is, if the classifier currently retained is F′(x), F′(x)+f_(n)(x)becomes a new classifier F(x). In addition, at this time, the featurequantity F_(n) of the feature point FP_(n) corresponding to the selectedweak classifier f_(n)(x) becomes a classification feature quantity.

In step S19, the weight setting unit 64 updates the weight W_(m) forevery learning image P_(m), and provides the updated weight of eachlearning image to the error-rate calculation unit 62.

For example, the classifier update unit 63 calculates a reliabilitydegree C_(n) expressed by the following Expression (3) on the basis ofthe error rate E_(n) of the weak classifier f_(n)(x) selected in stepS17, and provides its calculation result to the weight setting unit 64.

C _(n)=log((1−E _(n))/E _(n))  (3)

The weight setting unit 64 calculates the following Equation (4) on thebasis of the reliability degree C_(n) from the classifier update unit63, thereby re-calculating the weight W_(m) of each learning imageP_(m), and normalizing and updating all weights W_(m), and provides thenormalized and updated weights W_(m) to the error-rate calculation unit62.

W _(m) =W _(m) exp[−C _(n)·1_((y≠fn)) ], m= 1, 2, . . . M  (4)

In Expression (4), y≠fn indicates a condition of the feature point atwhich false recognition has occurred. Expression (4) indicates that theweight W_(m) of the learning image P_(m) including the feature pointFP_(n) at which false recognition has occurred is increased inrecognition by the selected weak classifier f_(n)(x). In addition, inExpression (4), the weight W_(m) of the learning image P_(m) in which nofalse recognition has occurred becomes an unchanged value.

Because the learning image P_(m) in which the false recognition hasoccurred is an image from which it is difficult to recognize a detectiontarget, it is possible to obtain a classifier capable of recognizing thedetection target with a higher accuracy in the transfer learning if aweight of each learning image is updated so that the weight of thisimage is increased.

In step S20, the classifier update unit 63 determines whether or not anecessary number of weak classifiers have been selected. For example, ifa classifier to be generated is specified to include J weak classifiers,a necessary number of weak classifiers are determined to have beenselected when the classifier to be generated includes the J weakclassifiers. That is, when the process of steps S15 to S19 is iterated Jtimes, a necessary number of weak classifiers are determined to havebeen selected.

If a necessary number of weak classifiers are determined not to havebeen selected in step S20, the process returns to step S15 and theabove-described process is iterated.

On the other hand, if a necessary number of weak classifiers aredetermined to have been selected in step S20, the process proceeds tostep S21.

In step S21, the classifier update unit 63 outputs the retainedclassifier F(x) and a classification feature quantity of each weakclassifier constituting the classifier to the classifier recording unit12, causes the classifier recording unit 12 to record the retainedclassifier F(x) and the classification feature quantity, and ends thetransfer learning process. For example, a sum of the selected J weakclassifiers becomes the classifier F(x) if the process of steps S15 toS19 have been performed J times.

As described above, the learning device 11 performs the boosting-basedtransfer learning using the learning image and the transfer classifieralready obtained by statistical learning.

If the weak classifiers of other classifiers already obtained are usedin the boosting-based learning obtained by a highly accurate classifieras described above, it is possible to obtain a highly accurateclassifier at a higher speed.

For example, if feature quantities are extracted from H feature pointsfor L learning images (learning samples) in normal boosting-basedlearning as illustrated in the upper side of FIG. 7, feature quantitiesare reordered for every L learning images with respect to feature pointsand weak classifiers are set. One optimum weak classifier is selectedfrom among the H obtained weak classifiers and added to the classifier,so that the classifier is updated and therefore a final classifier isobtained.

On the other hand, if the feature quantities of the H feature points areextracted from each of the L learning images and the classifier isgenerated in the boosting-based transfer learning as illustrated in thelower side of FIG. 7, Z (where Z<H) weak classifiers constituting theclassifier are transferred. One optimum weak classifier is selected fromamong the Z transferred weak classifiers and added to the classifier, sothat the classifier is updated and therefore a final classifier isobtained.

Therefore, it is preferable that a process be performed for featurepoints of which the number is Z less than H indicating the total numberof feature points on each learning image. In addition, because it isunnecessary to set a weak classifier, it is possible to obtain aclassifier at a speed that is L×(H/Z) times faster than in the normalboosting-based learning.

[Description of Classification Process]

If a classifier and a classification feature quantity are recorded onthe classifier recording unit 12 in the transfer learning processdescribed above, the recognition device 13 can detect a detection targetfrom a provided input image using the classifier and the classificationfeature quantity.

Hereinafter, the classification process by the recognition device 13will be described with reference to the flowchart of FIG. 8.

In step S41, the feature-point extraction unit 31 extracts somepositions (pixels) on the provided input image as feature points, andprovides the extracted feature points and the input image to thefeature-quantity calculation unit 32.

In step S42, the feature-quantity calculation unit 32 calculates featurequantities on the basis of the input image and the feature pointsprovided from the feature-point extraction unit 31, and providescalculation results to the classification calculation unit 33.

For example, the feature-quantity calculation unit 32 performs afiltering operation using a filter such as a rectangle filter withrespect to a feature point on the learning image, and designates itsfiltering result at the feature point as a feature quantity.

In steps S41 and S42, the same process as in steps S12 and S13 of FIG. 6is performed.

In step S43, the classification calculation unit 33 reads the classifierF(x) and the classification feature quantity from the classifierrecording unit 12, and carries out a calculation by substituting thefeature quantity into the read classifier. That is, the classificationcalculation unit 33 carries out a calculation by substituting a featurequantity corresponding to the classification feature quantity amongfeature quantities from the feature-quantity calculation unit 32 intothe classifier shown in Expression (2). Here, the feature quantity to besubstituted into a weak classifier constituting the classifier is afeature quantity of a feature point on the input image having the sameposition as the feature point of the learning image of which a featurequantity serving as the classification feature quantity is obtained.

The classification calculation unit 33 designates a numeric value “1”indicating that there is a detection target in the input image as aclassification result if an output value obtained by the calculation ofExpression (2) is greater than a threshold th, and designates a numericvalue “−1” indicating that there is no detection target in the inputimage as a classification result if the output value is less than orequal to the threshold th. The classification calculation unit 33provides the classification result obtained as described above to theclassification-result output unit 34.

In step S44, the classification-result output unit 34 outputs theclassification result provided from the classification calculation unit33, and ends the classification process. For example, theclassification-result output unit 34 causes a display unit (notillustrated) to display the fact of whether the detection target hasbeen detected or not been detected from the input image on the basis ofthe classification result.

As described above, the recognition device 13 detects the detectiontarget from the input image using the classifier and the classificationfeature quantity recorded on the classifier recording unit 12. It ispossible to detect the detection target with a higher accuracy bydetecting the detection target using a classifier obtained by theboosting-based transfer learning.

Second Embodiment

[Configuration Example of Learning Device]

Subsequently, the configuration of the learning device when theabove-described feature-quantity transfer learning of Technique 2 isperformed will be described.

FIG. 9 is a diagram illustrating a configuration example of anembodiment of the learning device when the feature-quantity transferlearning is performed. Parts corresponding to those of FIG. 4 aredenoted by the same reference numerals in FIG. 9, and descriptionthereof is properly omitted.

The learning device 91 includes a feature-point extraction unit 21, afeature-quantity calculation unit 22, an acquisition unit 23, and aclassifier generation unit 101.

The classifier generation unit 101 performs, for example, anAdaBoost-based transfer learning process, on the basis of a learningimage and a feature quantity provided from the feature quantitycalculation unit 22 and a classification feature quantity of each weakclassifier constituting a transfer classifier provided from theacquisition unit 23, thereby generating a classifier. In addition, theclassifier generation unit 101 provides the classifier recording unit 12with the generated classifier and the classification feature quantity ofthe weak classifier constituting the classifier, and causes theclassifier recording unit 12 to record the generated classifier and theclassification feature quantity.

[Configuration Example of Classifier Generation Unit]

In addition, the classifier generation unit 101 of FIG. 9 is configuredas illustrated in FIG. 10 in further detail. Parts corresponding tothose of FIG. 5 are denoted by the same reference numerals in FIG. 10,and description thereof is properly omitted.

The classifier generation unit 101 includes a weak-classifier settingunit 131, a weak-classification calculation unit 61, an error-ratecalculation unit 62, a classifier update unit 63, and a weight settingunit 64.

The weak-classifier setting unit 131 sets a weak classifier for everyfeature point using a feature quantity of a feature point identical withthe classification feature quantity of each weak classifier constitutingthe transfer classifier from the acquisition unit 23 among featurequantities of feature points of the learning image provided from thefeature-quantity calculation unit 22. In addition, theweak-classification setting unit 131 provides the weak-classificationcalculation unit 61 with the set weak classifier and a feature quantityof a feature point of each learning image.

[Description of Transfer Learning Process]

Next, the transfer learning process by the learning device 9 will bedescribed with reference to the flowchart of FIG. 11. In the transferlearning process, a plurality of learning images to which a label “1” isadded and a plurality of learning images to which a label “−1” is addedare provided to the learning device 91.

Because the process of steps S51 to S54 is the same as in steps S11 toS14 of FIG. 6, description thereof is omitted. However, in step S51, thetransfer classifier acquired by the acquisition unit 23 is provided tothe weak-classifier setting unit 131. In addition, in step S53, thefeature quantity extracted from each feature point of the learning imageis provided from the feature-quantity unit 22 to the weak-classifiersetting unit 131, and the label of the learning image is provided fromthe feature-quantity calculation unit 22 to the error-rate calculationunit 62.

In step S55, the weak-classifier setting unit 131 sets a weak classifieron the basis of a feature quantity of each feature point of the learningimage provided from the feature-quantity calculation unit 22 and aclassification feature quantity of each weak classifier constituting thetransfer classifier from the acquisition unit 23.

For example, k transfer classifiers are provided from the acquisitionunit 23 to the weak-classifier setting unit 131, and the total number ofweak classifiers constituting these transfer classifiers is assumed tobe N. That is, the N weak classifiers f₁(x) to f_(N)(x) are assumed tobe provided.

In this case, as illustrated in FIG. 12, feature quantities of N featurepoints FP_(n) corresponding to the weak classifiers f₁(x) to f_(N)(x)among feature quantities extracted from each learning image P_(m) (where1≦m≦M) are used and a weak classifier is set for every feature point.

In FIG. 12, feature quantities extracted from the learning image P_(m)are arranged in a horizontal direction. For example, in the drawing, A₁,A₂, A₃, . . . , A_(N) arranged on an uppermost side in the horizontaldirection indicate feature quantities corresponding to classificationfeature quantities of the weak classifiers f₁(x) to f_(N)(x) amongfeature quantities of feature points of the learning image P₁. That is,these are feature quantities of feature points on the learning image P₁in the same positions as those of feature points from which featurequantities serving as classification feature quantities are obtainedamong the feature quantities of feature points of the learning image P₁.

In addition, in the drawing of a character “P_(m)” indicating thelearning image, a number “+1” or “−1” of the left side indicates a labeladded to the learning image P_(m). That is, the number “+1” is a labelindicating that the detection target is included in the learning imageand the number “−1” is a label indicating that no detection target isincluded in the learning image.

Further, in FIG. 12, M feature quantities A_(n) to V_(n) arranged in avertical direction are grouped in one group Gr_(n) (where 1≦n≦N), andfeature quantities belonging to the group Gr_(n) become featurequantities of the same feature point in the learning images.

The weak-classifier setting unit 131 reorders M feature quantitiesbelonging to the group in descending or ascending order for every groupGr_(n).

The weak-classifier setting unit 131 sets a weak classifier byspecifying a function g(x>thw_(n)), a constant a_(n), and a constantb_(n) of the weak classifier shown in Expression (1) for every group onthe basis of the label of the learning image.

Specifically, as illustrated in FIG. 13, the feature quantities A₁ to V₁belonging to the group Gr₁ are sequentially arranged, and theweak-classifier setting unit 131 sets a threshold thw₁ specifying afunction g(x>thw₁) between feature quantities A₁ and C₁.

Here, there is no detection target to be recognized in a range in whichthe feature quantity is less than the threshold thw₁, that is, a rangeindicated by “−1” on the left side from the threshold thw₁. In addition,there is a detection target to be recognized in a range in which thefeature quantity is greater than the threshold thw₁, that is, a rangeindicated by “+1” on the right side of the drawing from the thresholdthw₁.

In this example, because the feature quantity A₁ surrounded by a dottedline in the drawing is a feature quantity of the learning imageincluding the detection target, this is regarded as an error (falserecognition). Likewise, because the feature quantities C₁ and V₁surrounded by the dotted line in the drawing are feature quantities ofthe learning images not including the detection target, these areregarded as an error (false recognition).

The weak-classifier setting unit 131 set a weak classifier bycalculating an error rate E₁ of the above-described weak classifierf₁(x) for a value of each threshold thw₁, for example, while varying thevalue of the threshold thw₁, and specifying the threshold thw₁ in whichthe error rate E₁ is minimized. In this case, the weak-classifiersetting unit 131 calculates the error rate by acquiring a weight of eachlearning image from the weight setting unit 64.

When setting a weak classifier with respect to each group, that is, afeature point of a learning image corresponding to a classificationfeature quantity of each weak classifier of a transfer classifier, theweak-classifier setting unit 131 provides the set weak classifier andthe feature quantity of each feature point of the learning image to theweak-classification calculation unit 61.

If the weak classifier is set as described above, then the process ofsteps S56 to S62 is performed, so that the transfer learning processends. However, because the process is the same as in steps S15 to S21 ofFIG. 6, description thereof is omitted.

However, while a process in which a weak classifier constituting thetransfer classifier is used is performed in step S15 of FIG. 6, aprocess in which the weak classifier set in the process of step S55 isused is performed in step S56 of FIG. 11.

As described above, the learning device 91 performs the boosting-basedtransfer learning using a learning image and a transfer classifieralready obtained by statistical learning.

It is possible to obtain a highly accurate classifier at a higher speedif classification feature quantities of weak classifiers of otherclassifiers already obtained are used in the boosting-based learning inwhich the highly accurate classifier can be obtained as described above.

For example, as illustrated in FIG. 14, if feature quantities of Hfeature points are extracted from each of L learning images in theboosting-based transfer learning and a classifier is generated,classification feature quantities of Z weak classifiers (where Z<H)constituting the classifier are transferred.

In addition, a weak classifier is set for every feature point usingfeature quantities of feature points of learning images corresponding tothe Z transferred classification feature quantities. One best weakclassifier of the set weak classifiers is selected and added to theclassifier, so that a final classifier is obtained by updating theclassifier.

On the other hand, because no classifier is transferred in the normalboosting-based learning, a weak classifier is set for every H featurepoints.

Therefore, because it is preferable that a process be performed forfeature points of which the number is Z less than H indicating the totalnumber of feature points on each learning image in the boosting-basedtransfer learning, it is possible to obtain a classifier at a speed thatis (H/Z) times faster than in the normal boosting-based learning.

If a classifier and a classification feature quantity generated by thelearning device 91 are also recorded on the classifier recording unit12, the classifier and the classification feature quantity are used fora classification process by the recognition device 13. That is, theseare used for the classification process described with reference to FIG.8.

Third Embodiment

[Classifier of Tree Structure]

Although an example in which a classifier for hand detection of othershapes already obtained is used in learning of a classifier fordetecting a specific hand shape has been described above, it is possibleto apply the transfer learning to a recognition system using aclassifier of a tree structure.

An example in which a hand shape on the input image is classified usinga classifier of a tree structure including 11 classifiers TF11 to TF21as illustrated in FIG. 15 will be described.

The tree-structure classifier is a classifier for multi-class objectrecognition, which detects a hand of any shape of a right or left handof a rock shape, the right or left hand of a paper shape, or the rightor left hand of a scissors shape from the input image.

The classifier TF11 constituting the tree-structure classifier is aclassifier for detecting the hand from the input image, andparticularly, is referred to as a root node. Here, if there is somethingsimilar to the hand in the input image without depending upon the handshape such as the rock or paper in hand detection by the classifierTF11, a recognition result indicating that the hand has been detected isoutput. In addition, the classifier TF12 is a classifier for detectingthe rock shape from the input image and the classifier TF13 is aclassifier for detecting the paper or scissors shape from the inputimage.

The classifiers TF14 and TF15 are each for detecting the right-hand rockand the left-hand rock from the input image, respectively, and theclassifiers TF16 and TF17 are classifiers for detecting the hand of thepaper shape and the hand of the scissors shape from the input image,respectively.

Further, the classifiers TF18 and TF19 are classifiers for detecting theright-hand paper and the left-hand paper from the input image,respectively, and the classifiers TF20 and TF21 are classifiers fordetecting the right-hand scissors and the left-hand scissors from theinput image, respectively.

In particular, the classifiers TF14, TF15, and TF18 to TF21 at ends of atree are referred to as leaves. The classifiers TF12, TF13, TF16, andTF17 are referred to as nodes between the root node and the leaves.

When the hand shape for use in the classifier of the tree structure isclassified, hand detection for an input image is performed by theclassifier TF11. If the hand is detected from the input image, theclassifier TF12 next performs rock detection from the input image andalso the classifier TF13 performs paper or scissors detection from theinput image.

At this time, if a classification result of the classifier TF12 is moreprobable than that of the classifier TF13, that is, if the hand of therock shape is estimated to be present in the input image,classifications by the classifiers TF14 and TF15 for the input image areperformed.

As a result, if a classification result of the classifier TF14 is moreprobable than that of the classifier TF15, the right-hand rock isassumed to have been detected from the input image. If a classificationresult of the classifier TF15 is more probable than that of theclassifier TF14, the left-hand rock is assumed to have been detectedfrom the input image.

In addition, if a classification result of the classifier TF13 is moreprobable than that of the classifier TF12, that is, if the hand of thepaper or scissors shape is estimated to be present in the input image,classifications by the classifiers TF16 and TF7 for the input image areperformed.

As a result, if a classification result of the classifier TF16 is moreprobable than that of the classifier TF17, that is, if the hand of thepaper shape is estimated to be present in the input image,classifications by the classifiers TF18 and TF19 for the input image isperformed. If the classification result of the classifier TF18 is moreprobable among the classification results of the classifiers, theright-hand paper is assumed to have been detected from the input image.If the classification result of the classifier TF19 is more probable,the left-hand paper is assumed to have been detected from the inputimage.

In addition, if a classification result of the classifier TF17 is moreprobable than that of the classifier TF16, that is, if the hand of thescissors shape is estimated to be present in the input image,classifications by the classifier TF20 and the classifier TF21 for theinput image are performed. If the classification result of theclassifier TF20 is more probable among the classification results of theclassifiers, the right-hand scissors are assumed to have been detectedfrom the input image. If the classification result of the classifierTF21 is more probable, the left-hand scissors are assumed to have beendetected from the input image.

As described above, object recognition for the input image is performedby some classifiers in the tree-structure classifier in whichmulti-class object recognition is possible. According to its result, anobject of any class among classes such as the right-hand paper and theleft-hand scissors is detected.

In addition, if a classifier serving as a leaf is generated by theboosting-based learning such as AdaBoost, for example, when thetree-structure classifier as described above is intended to be obtainedby learning, it is possible to obtain a highly accurate classifier.

Further, if a classifier of each node or root node is generated by theboosting-based transfer learning using classifiers serving as leaves asa transfer classifier, it is possible to obtain a highly accurateclassifier at a higher speed by fewer learning samples.

In particular, because a large number of learning images of hands ofmany shapes such as paper and a rock are necessary when a classifier ofa root node is generated by the normal boosting-based learning, anenormous calculation time is necessary. On the other hand, it ispossible to significantly improve learning efficiency if the classifierof the leaf is transferred and the classifier of the root node isgenerated by the transfer learning.

For example, it is preferable that transfer learning be performed usingthe leaf classifiers TF14, TF15, and TF18 to TF21 as the transferclassifiers when the node classifier TF12 is intended to be obtained.

Specifically, it is possible to obtain the classifier TF12, for example,if the learning device 11 illustrated in FIG. 4 performs the transferlearning process of FIG. 6 by transferring weak classifiers constitutingthe classifiers TF14, TF15, and TF18 to TF21.

In addition, of course, it is possible to obtain the classifier TF12,for example, if the learning device 91 illustrated in FIG. 9 performsthe transfer learning process of FIG. 11 by transferring classificationfeature quantities of the weak classifiers constituting the classifiersTF14, TF15, and TF18 to TF21.

[Configuration Example of Recognition Device]

Next, the recognition device, which performs multi-class hand-shaperecognition using the tree-structure classifier illustrated in FIG. 15,will be described. This recognition device is configured, for example,as illustrated in FIG. 16.

That is, the recognition device 161 includes a hand classification unit171, a rock classification unit 172, a scissors/paper classificationunit 173, a comparison unit 174, a right-rock classification unit 175, aleft-rock classification unit 176, a paper classification unit 177, ascissors classification unit 178, a comparison unit 179, a right-paperclassification unit 180, a left-paper classification unit 181, aright-scissors classification unit 182, a left-scissors classificationunit 183, and an output unit 184.

In the recognition device 161, the classifiers TF11 to TF21 of FIG. 15are each recorded on the hand classification unit 171, the rockclassification unit 172, the scissors/paper classification unit 173, theright-rock classification unit 175, the left-rock classification unit176, the paper classification unit 177, the scissors classification unit178, the right-paper classification unit 180, the left-paperclassification unit 181, the right-scissors classification unit 182, andthe left-scissors classification unit 183.

The hand classification unit 171 detects a hand from an input imageprovided using the classifier TF11. If the hand has been detected, thehand classification unit 171 provides a classification result indicatingthat the hand has been detected and the input image to the rockclassification unit 172 and the scissors/paper classification unit 173.In addition, the hand classification unit 171 notifies the output unit184 of the fact that no hand has been detected if no hand has beendetected from the input image.

The rock classification unit 172 and the scissors/paper classificationunit 173 classify the rock and the paper or scissors on the input imageprovided from the hand classification unit 171 on the basis of therecorded classifiers TF12 and TF13, and provide the comparison unit 174with classification results and the input image.

The comparison unit 174 provides the input image to the right-rockclassification unit 175 and the left-rock classification unit 176 or thepaper classification unit 177 and the scissors classification unit 178by comparing the classification results provided from the rockclassification unit 172 and the scissors/paper classification unit 173.

The right-rock classification unit 175 and the left-rock classificationunit 176 classify the right-hand rock and the left-hand rock on theinput image provided from the comparison unit 174 on the basis of therecorded classifiers TF14 and TF15, and provide the output unit 184 withtheir classification results.

The paper classification unit 177 and the scissors classification unit178 classify the paper and the scissors on the input image provided fromthe comparison unit 174 on the basis of the recorded classifiers TF16and TF17, and provide the comparison unit 179 with their classificationresults and the input image.

The comparison unit 179 provides the input image to the right-paperclassification unit 180 and the left-paper classification unit 181 orthe right-scissors classification unit 182 and the left-scissorsclassification unit 183 by comparing the classification results suppliedfrom the paper classification unit 177 and the scissors classificationunit 178.

The right-paper classification unit 180 and the left-paperclassification unit 181 classify the right-hand paper and the left-handpaper on the input image provided from the comparison unit 179 on thebasis of the recorded classifiers TF18 and TF19, and provide theirclassification results to the output unit 184. The right-scissorsclassification unit 182 and the left-scissors classification unit 183classify the right-hand scissors and the left-hand scissors on the inputimage provided from the comparison unit 179 on the basis of the recordedclassifiers TF20 and TF21, and provide their classification results tothe output unit 184.

The output unit 184 outputs a hand classification result from the inputimage on the basis of the classification results from the handclassification unit 171, the right-rock classification unit 175, theleft-rock classification unit 176, the right-paper classification unit180, the left-paper classification unit 181, the right-scissorsclassification unit 182, and the left-scissors classification unit 183.

[Description of Classification Process]

If an input image is provided to the recognition device 161 of FIG. 16and a hand-shape recognition instruction is generated, the recognitiondevice 161 detects a hand from the input image by performing theclassification process. Hereinafter, the classification process by therecognition device 161 will be described with reference to the flowchartof FIG. 17.

In step S91, the hand recognition unit 171 detects the hand from theinput image provided using the classifier TF11. That is, the handclassification unit 171 extracts a feature quantity from the input imageand substitutes the feature quantity into the classifier TF11. If itscalculation result is greater than a predetermined threshold, the handis assumed to have been detected from the input image.

In step S92, the hand classification unit 171 determines whether or notthe hand has been detected from the input image. If no hand isdetermined to have been detected in step S92, its determination resultis provided to the output unit 184 and the process proceeds to stepS105.

On the other hand, if the hand is determined to have been detected instep S92, the hand classification unit 171 provides the rockclassification unit 172 and the scissors/paper classification unit 173with the classification result indicating that the hand has beendetected, and the process proceeds to step S93.

In step S93, the rock classification unit 172 performs theclassification of a rock from the input image provided from the handclassification unit 171 on the basis of the recorded classifier TF12.That is, the rock classification unit 172 extracts a feature quantityfrom the input image, substitutes the extracted feature quantity intothe classifier TF12, and provides the comparison unit 174 with an outputvalue obtained as its calculation result and the input image.

The output value obtained as described above indicates rock-handlikelihood of the input image. When the output value is large, the inputimage is likely to be an image of a rock-shaped hand. In otherclassifiers, an output value obtained by substituting a feature quantityinto a classifier indicates the likelihood of an object detected by theclassifier, that is, a specific hand shape herein.

In step S94, the scissors/paper classification unit 173 performs theclassification of the paper or scissors from the input image providedfrom the hand classification unit 171 on the basis of the recordedclassifier TF13. That is, the scissors/paper classification unit 173extracts a feature quantity from the input image, substitutes theextracted feature quantity into the classifier TF13, and provides thecomparison unit 174 with an output value obtained as its calculationresult and the input image.

In step S95, the comparison unit 174 determines whether or not the rockon the input image has been classified by comparing the output valuefrom the rock classification unit 172 to the output value from thescissors/paper classification unit 173. For example, if the output valuefrom the rock classification unit 172 is greater than the output valuefrom the scissors/paper classification unit 173, the rock is determinedto have been classified because the rock is more likely to be includedin the input image than the scissors or the paper.

If the rock is determined to have been classified in step S95, thecomparison unit 174 provides the input image to the right-rockclassification unit 175 and the left-rock classification unit 176, andthe process proceeds to step S96.

In step S96, the right-rock classification unit 175 performs theclassification of a right-hand rock from the input image provided fromthe comparison unit 174 on the basis of the recorded classifier TF14.That is, the right-rock classification unit 175 extracts a featurequantity from the input image, substitutes the extracted featurequantity into the classifier TF14, and provides the output unit 184 withan output value obtained as its calculation result.

In step S97, the left-rock classification unit 176 performs theclassification of a left-hand rock from the input image provided fromthe comparison unit 174 on the basis of the recorded classifier TF15.That is, the left-rock classification unit 176 extracts a featurequantity from the input image, substitutes the extracted featurequantity into the classifier TF15, and provides the output unit 184 withan output value obtained as its calculation result.

If the process of step S97 is performed, then the process proceeds tostep S105.

In addition, if no rock is determined to have been classified on theinput image in step S95, that is, if the paper or the scissors on theinput image has been classified, the comparison unit 174 provides theinput image to the paper classification unit 177 and the scissorsclassification unit 178 and the process proceeds to step S98.

In step S98, the paper classification unit 177 performs theclassification of the paper from the input image provided from thecomparison unit 174 on the basis of the recorded classifier TF16. Thatis, the paper classification unit 177 extracts a feature quantity fromthe input image, substitutes the extracted feature quantity into theclassifier TF16, and provides the comparison unit 179 with an outputvalue obtained as its calculation result and the input image.

In step S99, the scissors classification unit 178 performs theclassification of the scissors from the input image provided from thecomparison unit 174 on the basis of the recorded classifier TF17. Thatis, the scissors classification unit 178 extracts a feature quantityfrom the input image, substitutes the extracted feature quantity intothe classifier TF17, and provides the comparison unit 179 with an outputvalue obtained as its calculation result and the input image.

In step S100, the comparison unit 179 determines whether the paper onthe input image has been classified by comparing the output value fromthe paper classification unit 177 to the output value from the scissorsclassification unit 178. For example, if the output value from the paperclassification unit 177 is greater than the output value from thescissors classification unit 178, the paper is determined to have beenclassified.

If the paper is determined to have been classified in step S100, thecomparison unit 179 provides the input image to the right-paperclassification unit 180 and the left-paper classification unit 181 andthe process proceeds to step S101.

In step S101, the right-paper classification unit 180 classifies theright-hand paper from the input image provided from the comparison unit179 on the basis of the recorded classifier TF18. That is, theright-paper classification unit 180 extracts a feature quantity from theinput image, substitutes the extracted feature quantity into theclassifier TF18, and provides the output unit 184 with an output valueobtained as its calculation result.

In step S102, the left-paper classification unit 181 classifies theleft-hand paper from the input image provided from the comparison unit179 on the basis of the recorded classifier TF19. That is, theleft-paper classification unit 181 extracts a feature quantity from theinput image, substitutes the extracted feature quantity into theclassifier TF19, and provides the output unit 184 with an output valueobtained as its calculation result.

If the process of step S102 is performed, then the process proceeds tostep S105.

Further, if no paper is determined to have been classified, that is, ifthe scissors are determined to have been classified in step S100, thecomparison unit 179 provides the input image to the right-scissorsclassification unit 182 and the left-scissors classification unit 183and the process proceeds to step S103.

In step S103, the right-scissors classification unit 182 classifies theright-hand scissors from the input image provided from the comparisonunit 179 on the basis of the recorded classifier TF20. That is, theright-scissors classification unit 182 extracts a feature quantity fromthe input image, substitutes the extracted feature quantity into theclassifier TF20, and provides the output unit 184 with an output valueobtained as its calculation result.

In step S104, the left-scissors classification unit 183 classifies theleft-hand scissors from the input image provided from the comparisonunit 179 on the basis of the recorded classifier TF21. That is, theleft-scissors classification unit 183 extracts a feature quantity fromthe input image, substitutes the extracted feature quantity into theclassifier TF21, and provides the output unit 184 with an output valueobtained as its calculation result.

If the process of step S104 is performed, then the process proceeds tostep S105.

If no hand is determined to have been detected in step S92, if theleft-hand rock has been classified in step S97, if the left-hand paperhas been classified in step S102, or if the left-hand scissors have beenclassified in step S104, the process of step S105 is performed.

That is, in step S105, the output unit 184 outputs a final handclassification result from the input image on the basis of theclassification results from the hand classification unit 171, theright-rock classification unit 175 and the left-rock classification unit176, the right-paper classification unit 180 and the left-paperclassification unit 181, or the right-scissors classification unit 182and the left-scissors classification unit 183.

Specifically, if no hand is determined to have been detected in stepS92, that is, if a classification result indicating that no hand hasbeen detected has been provided from the hand classification unit 171,the output unit 184 outputs the classification result as a finalclassification result.

In addition, if the output values from the right-rock classificationunit 175 and the left-rock classification unit 176 have been providedaccording to the process of steps S96 and S97, the output unit 184outputs the classification result indicated by a larger output valuebetween the output values as a final classification result. For example,if the output value from the right-rock classification unit 175 islarger, the classification result indicating that the right-hand rockhas been classified is output.

Further, if the output values from the right-paper classification unit180 and the left-paper classification unit 181 have been providedaccording to the process of steps S101 and S102, the output unit 184outputs the classification result indicated by a larger output valuebetween the output values as a final classification result. In addition,if the output values from the right-scissors classification unit 182 andthe left-scissors classification unit 183 have been provided accordingto the process of steps S103 and S104, the output unit 184 outputs theclassification result indicated by a larger output value between theoutput values as a final classification result.

If the final classification result is output from the output unit 184 asdescribed above, the classification process ends.

As described above, the recognition device 161 classifies an object onthe input image using a classifier of a tree structure, and outputs itsclassification result. If the classifier obtained by boosting-basedtransfer learning is used as the tree-structure classifier, it ispossible to classify the object with a high accuracy.

The above-described series of processing may be performed by hardware ormay be performed by software. When the series of processing is performedby software, a program forming the software is installed into a computerthat is incorporated in a dedicated hardware, or installed from aprogram storage medium into a general-purpose personal computer, forexample, that can perform various types of functions by installingvarious types of programs.

FIG. 18 is a block diagram showing a hardware configuration example of acomputer that performs the above-described series of processing using aprogram.

In the computer, a central processing unit (CPU) 301, a read only memory(ROM) 302 and a random access memory (RAM) 303 are mutually connected bya bus 304.

Further, an input/output interface 305 is connected to the bus 304.Connected to the input/output interface 305 are an input portion 306formed by a keyboard, a mouse, a microphone and the like, an outputportion 307 formed by a display, a speaker and the like, a storageportion 308 formed by a hard disk, a nonvolatile memory and the like, acommunication portion 309 formed by a network interface and the like,and a drive 310 that drives a removable media 311 that is a magneticdisk, an optical disk, a magneto-optical disk, or a semiconductor me

In the computer configured as described above, the CPU 301 loads aprogram that is stored, for example, in the storage portion 308 onto theRAM 303 via the input/output interface 305 and the bus 304, and executesthe program. Thus, the above-described series of processing isperformed.

The program executed by the computer (the CPU 301) is recorded in theremovable media 311, which is a package media formed by, for example, amagnetic disc (including a flexible disk), an optical disk (a compactdisc read only memory (CD-ROM), a digital versatile disc (DVD) or thelike), a magneto optical disk, or a semiconductor memory etc.Alternatively, the program is provided via a wired or wirelesstransmission media, such as a local area network, the Internet and adigital satellite broadcast.

Then, by inserting the removable media 311 into the drive 310, theprogram can be installed in the storage portion 908 via the input/outputinterface 305. Further, the program can be received by the communicationportion 309 via a wired or wireless transmission media and installed inthe storage portion 908. Moreover, the program can be installed inadvance in the ROM 302 or the storage portion 908.

It should be noted that the program executed by a computer may be aprogram that is processed in time series according to the sequencedescribed in this specification or a program that is processed inparallel or at necessary timing such as upon calling.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

Additionally, the present technology may also be configured as below.

(1)

A learning device including:

a feature-quantity extraction unit for extracting a feature quantityfrom a feature point of a learning image with respect to each of aplurality of learning images including a learning image including adetection target and a learning image not including the detectiontarget;

a weak-classification calculation unit for calculating a classificationresult of the detection target according to a weak classifier for everylearning image by substituting the feature quantity corresponding to theweak classifier into the weak classifier with respect to each of aplurality of weak classifiers constituting a transfer classifier, whichis a classifier for detecting the detection target obtained bystatistical learning; and

a classifier generation unit for generating the classifier for detectingthe detection target using the weak classifier selected from theplurality of weak classifiers on the basis of the classification result.

(2)

The learning device according to (1), further including:

a weight setting unit for setting a weight of the learning image basedon the classification result; and

an error-rate calculation unit for calculating an error rate of the weakclassifier based on the classification result of each learning imageaccording to the weak classifier and the weight,

wherein the classifier generation unit selects the weak classifier basedon the error rate.

(3)

The learning device according to (1) or (2), wherein the classifiergenerated by the classifier generation unit is used for multi-classobject recognition.

(4)

The learning device according to any one of claims (1) to (3), wherein:

the classifier generated by the classifier generation unit is aclassifier constituting a classifier of a tree structure, and

the transfer classifier is a classifier constituting a leaf of theclassifier of the tree structure.

(5)

A learning method for use in a learning device including afeature-quantity extraction unit for extracting a feature quantity froma feature point of a learning image with respect to each of a pluralityof learning images including a learning image including a detectiontarget and a learning image not including the detection target, aweak-classification calculation unit for calculating a classificationresult of the detection target according to a weak classifier for everylearning image by substituting the feature quantity corresponding to theweak classifier into the weak classifier with respect to each of aplurality of weak classifiers constituting a transfer classifier, whichis a classifier for detecting the detection target obtained bystatistical learning, and a classifier generation unit for generatingthe classifier for detecting the detection target using the weakclassifier selected from the plurality of weak classifiers on the basisof the classification result, the learning method including:

extracting, by the feature-quantity extraction unit, the featurequantity from the learning image;

calculating, by the weak-classification calculation unit, theclassification result; and

generating, by the classifier generation unit, the classifier.

(6)

A program for causing a computer to execute:

extracting a feature quantity from a feature point of a learning imagewith respect to each of a plurality of learning images including alearning image including a detection target and a learning image notincluding the detection target;

calculating a classification result of the detection target according toa weak classifier for every learning image by substituting the featurequantity corresponding to the weak classifier into the weak classifierwith respect to each of a plurality of weak classifiers constituting atransfer classifier, which is a classifier for detecting the detectiontarget obtained by statistical learning; and

generating the classifier for detecting the detection target using theweak classifier selected from the plurality of weak classifiers on thebasis of the classification result.

(7)

A learning device including:

a feature-quantity extraction unit for extracting a feature quantityfrom a feature point of a learning image with respect to each of aplurality of learning images including a learning image including adetection target and a learning image not including the detectiontarget;

a weak-classifier setting unit for generating a weak classifier based onthe feature quantity corresponding to a transfer weak classifierconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, among featurequantities extracted from the learning image and the learning image;

a weak-classification calculation unit for calculating a classificationresult of the detection target according to the weak classifier forevery learning image by substituting the feature quantity correspondingto the weak classifier into the weak classifier; and

a classifier generation unit for generating the classifier for detectingthe detection target using the weak classifier selected from theplurality of weak classifiers on the basis of the classification result.

(8)

The learning device according to (7), further including:

a weight setting unit for setting a weight of the learning image basedon the classification result; and

an error-rate calculation unit for calculating an error rate of the weakclassifier based on the classification result of each learning imageaccording to the weak classifier and the weight,

wherein the classifier generation unit selects the weak classifier basedon the error rate.

(9)

The learning device according to (7) or (8), wherein the classifiergenerated by the classifier generation unit is used for multi-classobject recognition.

(10)

The learning device according to any one of (7) to (9), wherein:

the classifier generated by the classifier generation unit is aclassifier constituting a classifier of a tree structure, and

the transfer classifier is a classifier constituting a leaf of theclassifier of the tree structure.

(11)

A learning method for use in a learning device including afeature-quantity extraction unit for extracting a feature quantity froma feature point of a learning image with respect to each of a pluralityof learning images including a learning image including a detectiontarget and a learning image not including the detection target, aweak-classifier setting unit for generating a weak classifier based onthe feature quantity corresponding to a transfer weak classifierconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, among featurequantities extracted from the learning image and the learning image, aweak-classification calculation unit for calculating a classificationresult of the detection target according to the weak classifier forevery learning image by substituting the feature quantity correspondingto the weak classifier into the weak classifier, and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak classifiers on the basis of the classification result, thelearning method including: extracting, by the feature-quantityextraction unit, the feature quantity from the learning image;

generating, by the weak-classifier setting unit, the weak classifier;

calculating, by the weak-classification calculation unit, theclassification result; and

generating, by the classifier generation unit, the classifier.

(12)

A program for causing a computer to execute:

extracting a feature quantity from a feature point of a learning imagewith respect to each of a plurality of learning images including alearning image including a detection target and a learning image notincluding the detection target;

generating a weak classifier based on the feature quantity correspondingto a transfer weak classifier constituting a transfer classifier, whichis a classifier for detecting the detection target obtained bystatistical learning, among feature quantities extracted from thelearning image and the learning image;

calculating a classification result of the detection target according tothe weak classifier for every learning image by substituting the featurequantity corresponding to the weak classifier into the weak classifier;and

generating the classifier for detecting the detection target using theweak classifier selected from the plurality of weak classifiers on thebasis of the classification result.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

The present disclosure contains subject matter related to that disclosedin Japanese Priority Patent Application JP 2011-114379 filed in theJapan Patent Office on May 23, 2011, the entire content of which ishereby incorporated by reference.

1. A learning device comprising: a feature-quantity extraction unit forextracting a feature quantity from a feature point of a learning imagewith respect to each of a plurality of learning images including alearning image including a detection target and a learning image notincluding the detection target; a weak-classification calculation unitfor calculating a classification result of the detection targetaccording to a weak classifier for every learning image by substitutingthe feature quantity corresponding to the weak classifier into the weakclassifier with respect to each of a plurality of weak classifiersconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning; and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak classifiers on the basis of the classification result.
 2. Thelearning device according to claim 1, further comprising: a weightsetting unit for setting a weight of the learning image based on theclassification result; and an error-rate calculation unit forcalculating an error rate of the weak classifier based on theclassification result of each learning image according to the weakclassifier and the weight, wherein the classifier generation unitselects the weak classifier based on the error rate.
 3. The learningdevice according to claim 2, wherein the classifier generated by theclassifier generation unit is used for multi-class object recognition.4. The learning device according to claim 3, wherein: the classifiergenerated by the classifier generation unit is a classifier constitutinga classifier of a tree structure, and the transfer classifier is aclassifier constituting a leaf of the classifier of the tree structure.5. A learning method for use in a learning device including afeature-quantity extraction unit for extracting a feature quantity froma feature point of a learning image with respect to each of a pluralityof learning images including a learning image including a detectiontarget and a learning image not including the detection target, aweak-classification calculation unit for calculating a classificationresult of the detection target according to a weak classifier for everylearning image by substituting the feature quantity corresponding to theweak classifier into the weak classifier with respect to each of aplurality of weak classifiers constituting a transfer classifier, whichis a classifier for detecting the detection target obtained bystatistical learning, and a classifier generation unit for generatingthe classifier for detecting the detection target using the weakclassifier selected from the plurality of weak classifiers on the basisof the classification result, the learning method comprising:extracting, by the feature-quantity extraction unit, the featurequantity from the learning image; calculating, by theweak-classification calculation unit, the classification result; andgenerating, by the classifier generation unit, the classifier.
 6. Aprogram for causing a computer to execute: extracting a feature quantityfrom a feature point of a learning image with respect to each of aplurality of learning images including a learning image including adetection target and a learning image not including the detectiontarget; calculating a classification result of the detection targetaccording to a weak classifier for every learning image by substitutingthe feature quantity corresponding to the weak classifier into the weakclassifier with respect to each of a plurality of weak classifiersconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning; and generatingthe classifier for detecting the detection target using the weakclassifier selected from the plurality of weak classifiers on the basisof the classification result.
 7. A learning device comprising: afeature-quantity extraction unit for extracting a feature quantity froma feature point of a learning image with respect to each of a pluralityof learning images including a learning image including a detectiontarget and a learning image not including the detection target; aweak-classifier setting unit for generating a weak classifier based onthe feature quantity corresponding to a transfer weak classifierconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, among featurequantities extracted from the learning image and the learning image; aweak-classification calculation unit for calculating a classificationresult of the detection target according to the weak classifier forevery learning image by substituting the feature quantity correspondingto the weak classifier into the weak classifier; and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak classifiers on the basis of the classification result.
 8. Thelearning device according to claim 7, further comprising: a weightsetting unit for setting a weight of the learning image based on theclassification result; and an error-rate calculation unit forcalculating an error rate of the weak classifier based on theclassification result of each learning image according to the weakclassifier and the weight, wherein the classifier generation unitselects the weak classifier based on the error rate.
 9. The learningdevice according to claim 8, wherein the classifier generated by theclassifier generation unit is used for multi-class object recognition.10. The learning device according to claim 9, wherein: the classifiergenerated by the classifier generation unit is a classifier constitutinga classifier of a tree structure, and the transfer classifier is aclassifier constituting a leaf of the classifier of the tree structure.11. A learning method for use in a learning device including afeature-quantity extraction unit for extracting a feature quantity froma feature point of a learning image with respect to each of a pluralityof learning images including a learning image including a detectiontarget and a learning image not including the detection target, aweak-classifier setting unit for generating a weak classifier based onthe feature quantity corresponding to a transfer weak classifierconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, among featurequantities extracted from the learning image and the learning image, aweak-classification calculation unit for calculating a classificationresult of the detection target according to the weak classifier forevery learning image by substituting the feature quantity correspondingto the weak classifier into the weak classifier, and a classifiergeneration unit for generating the classifier for detecting thedetection target using the weak classifier selected from the pluralityof weak classifiers on the basis of the classification result, thelearning method comprising: extracting, by the feature-quantityextraction unit, the feature quantity from the learning image;generating, by the weak-classifier setting unit, the weak classifier;calculating, by the weak-classification calculation unit, theclassification result; and generating, by the classifier generationunit, the classifier.
 12. A program for causing a computer to execute:extracting a feature quantity from a feature point of a learning imagewith respect to each of a plurality of learning images including alearning image including a detection target and a learning image notincluding the detection target; generating a weak classifier based onthe feature quantity corresponding to a transfer weak classifierconstituting a transfer classifier, which is a classifier for detectingthe detection target obtained by statistical learning, among featurequantities extracted from the learning image and the learning image;calculating a classification result of the detection target according tothe weak classifier for every learning image by substituting the featurequantity corresponding to the weak classifier into the weak classifier;and generating the classifier for detecting the detection target usingthe weak classifier selected from the plurality of weak classifiers onthe basis of the classification result.